Abstract
Detection of falls of elderly people is a trivial yet an immediate problem due to the growing age of the population. This demands the need for autonomous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was achieved through transfer learning by extending the idea of action recognition using dynamic images which is a standard RGB image containing the appearance and dynamics of a whole video sequence. Such information is vital in dealing with applications like human action recognition. Since we are also looking for a cheap and scalable solution, the use of a 360\(^\circ \) camera seems reasonable and reliable. The top view provided by this camera gives a better perspective than any other alternatives by giving an un-obstructive view of the subjects.
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Saurav, S., Madhu Kiran, T.N.D., Sravan Kumar Reddy, B., Sanjay Srivastav, K., Singh, S., Saini, R. (2019). Dynamic Image Networks for Human Fall Detection in 360-degree Videos. In: Arora, C., Mitra, K. (eds) Computer Vision Applications. WCVA 2018. Communications in Computer and Information Science, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-15-1387-9_6
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